Overview

Dataset statistics

Number of variables20
Number of observations827
Missing cells834
Missing cells (%)5.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory129.3 KiB
Average record size in memory160.2 B

Variable types

Numeric11
Categorical9

Alerts

trestbps is highly correlated with trestbpdHigh correlation
met is highly correlated with df_index and 2 other fieldsHigh correlation
thalach is highly correlated with metHigh correlation
trestbpd is highly correlated with trestbps and 1 other fieldsHigh correlation
oldpeak is highly correlated with exang and 1 other fieldsHigh correlation
num is highly correlated with oldpeakHigh correlation
df_index is highly correlated with restecg and 2 other fieldsHigh correlation
cp is highly correlated with exangHigh correlation
restecg is highly correlated with df_indexHigh correlation
tpeakbpd is highly correlated with trestbpdHigh correlation
exang is highly correlated with cp and 1 other fieldsHigh correlation
dataset is highly correlated with df_index and 1 other fieldsHigh correlation
trestbps has 60 (7.3%) missing values Missing
htn has 33 (4.0%) missing values Missing
fbs has 66 (8.0%) missing values Missing
pro has 65 (7.9%) missing values Missing
met has 107 (12.9%) missing values Missing
thalach has 57 (6.9%) missing values Missing
thalrest has 58 (7.0%) missing values Missing
tpeakbps has 64 (7.7%) missing values Missing
tpeakbpd has 64 (7.7%) missing values Missing
trestbpd has 60 (7.3%) missing values Missing
exang has 57 (6.9%) missing values Missing
oldpeak has 60 (7.3%) missing values Missing
rldv5e has 71 (8.6%) missing values Missing
df_index has unique values Unique
oldpeak has 322 (38.9%) zeros Zeros

Reproduction

Analysis started2022-10-17 20:35:48.484081
Analysis finished2022-10-17 20:35:58.114598
Duration9.63 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct827
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean430.9226119
Minimum0
Maximum900
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-10-17T22:35:58.167318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile41.3
Q1206.5
median413
Q3620.5
95-th percentile858.7
Maximum900
Range900
Interquartile range (IQR)414

Descriptive statistics

Standard deviation263.2545032
Coefficient of variation (CV)0.6109090031
Kurtosis-1.150818063
Mean430.9226119
Median Absolute Deviation (MAD)207
Skewness0.1687656497
Sum356373
Variance69302.93347
MonotonicityStrictly increasing
2022-10-17T22:35:58.247399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.1%
5551
 
0.1%
5451
 
0.1%
5461
 
0.1%
5471
 
0.1%
5481
 
0.1%
5491
 
0.1%
5501
 
0.1%
5511
 
0.1%
5521
 
0.1%
Other values (817)817
98.8%
ValueCountFrequency (%)
01
0.1%
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
ValueCountFrequency (%)
9001
0.1%
8991
0.1%
8981
0.1%
8971
0.1%
8961
0.1%
8951
0.1%
8941
0.1%
8931
0.1%
8921
0.1%
8911
0.1%

age
Real number (ℝ≥0)

Distinct49
Distinct (%)5.9%
Missing2
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean53.30181818
Minimum28
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-10-17T22:35:58.325003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile37
Q147
median54
Q360
95-th percentile68
Maximum77
Range49
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.489509235
Coefficient of variation (CV)0.1780334998
Kurtosis-0.3877835298
Mean53.30181818
Median Absolute Deviation (MAD)7
Skewness-0.1563824897
Sum43974
Variance90.05078553
MonotonicityNot monotonic
2022-10-17T22:35:58.404585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
5448
 
5.8%
5838
 
4.6%
5536
 
4.4%
5233
 
4.0%
5732
 
3.9%
5132
 
3.9%
5931
 
3.7%
6231
 
3.7%
5630
 
3.6%
4830
 
3.6%
Other values (39)484
58.5%
ValueCountFrequency (%)
281
 
0.1%
293
 
0.4%
301
 
0.1%
312
 
0.2%
325
0.6%
332
 
0.2%
347
0.8%
359
1.1%
365
0.6%
3711
1.3%
ValueCountFrequency (%)
772
 
0.2%
762
 
0.2%
753
 
0.4%
747
0.8%
724
 
0.5%
715
 
0.6%
705
 
0.6%
6912
1.5%
688
1.0%
6713
1.6%

sex
Categorical

Distinct2
Distinct (%)0.2%
Missing2
Missing (%)0.2%
Memory size6.6 KiB
1.0
643 
0.0
182 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2475
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0643
77.8%
0.0182
 
22.0%
(Missing)2
 
0.2%

Length

2022-10-17T22:35:58.474378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-17T22:35:58.535836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0643
77.9%
0.0182
 
22.1%

Most occurring characters

ValueCountFrequency (%)
01007
40.7%
.825
33.3%
1643
26.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1650
66.7%
Other Punctuation825
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01007
61.0%
1643
39.0%
Other Punctuation
ValueCountFrequency (%)
.825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2475
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01007
40.7%
.825
33.3%
1643
26.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01007
40.7%
.825
33.3%
1643
26.0%

cp
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.5%
Missing2
Missing (%)0.2%
Memory size6.6 KiB
4.0
426 
3.0
190 
2.0
166 
1.0
43 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2475
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row2.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
4.0426
51.5%
3.0190
23.0%
2.0166
 
20.1%
1.043
 
5.2%
(Missing)2
 
0.2%

Length

2022-10-17T22:35:58.587957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-17T22:35:58.659977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
4.0426
51.6%
3.0190
23.0%
2.0166
 
20.1%
1.043
 
5.2%

Most occurring characters

ValueCountFrequency (%)
.825
33.3%
0825
33.3%
4426
17.2%
3190
 
7.7%
2166
 
6.7%
143
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1650
66.7%
Other Punctuation825
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0825
50.0%
4426
25.8%
3190
 
11.5%
2166
 
10.1%
143
 
2.6%
Other Punctuation
ValueCountFrequency (%)
.825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2475
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.825
33.3%
0825
33.3%
4426
17.2%
3190
 
7.7%
2166
 
6.7%
143
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII2475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.825
33.3%
0825
33.3%
4426
17.2%
3190
 
7.7%
2166
 
6.7%
143
 
1.7%

trestbps
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct58
Distinct (%)7.6%
Missing60
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean131.7014342
Minimum80
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-10-17T22:35:58.725300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile105
Q1120
median130
Q3140
95-th percentile160
Maximum200
Range120
Interquartile range (IQR)20

Descriptive statistics

Standard deviation18.25102838
Coefficient of variation (CV)0.1385788128
Kurtosis0.5614984919
Mean131.7014342
Median Absolute Deviation (MAD)10
Skewness0.5820909421
Sum101015
Variance333.1000371
MonotonicityNot monotonic
2022-10-17T22:35:58.805618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120118
14.3%
130106
12.8%
14093
 
11.2%
11054
 
6.5%
15050
 
6.0%
16041
 
5.0%
12525
 
3.0%
12816
 
1.9%
10015
 
1.8%
13814
 
1.7%
Other values (48)235
28.4%
(Missing)60
 
7.3%
ValueCountFrequency (%)
801
 
0.1%
921
 
0.1%
942
 
0.2%
956
 
0.7%
961
 
0.1%
981
 
0.1%
10015
1.8%
1011
 
0.1%
1023
 
0.4%
1043
 
0.4%
ValueCountFrequency (%)
2002
 
0.2%
1921
 
0.1%
1902
 
0.2%
18011
 
1.3%
1783
 
0.4%
1741
 
0.1%
1722
 
0.2%
17012
 
1.5%
1651
 
0.1%
16041
5.0%

htn
Categorical

MISSING

Distinct2
Distinct (%)0.3%
Missing33
Missing (%)4.0%
Memory size6.6 KiB
0.0
401 
1.0
393 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2382
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0401
48.5%
1.0393
47.5%
(Missing)33
 
4.0%

Length

2022-10-17T22:35:58.876197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-17T22:35:58.935337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0401
50.5%
1.0393
49.5%

Most occurring characters

ValueCountFrequency (%)
01195
50.2%
.794
33.3%
1393
 
16.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1588
66.7%
Other Punctuation794
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01195
75.3%
1393
 
24.7%
Other Punctuation
ValueCountFrequency (%)
.794
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2382
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01195
50.2%
.794
33.3%
1393
 
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2382
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01195
50.2%
.794
33.3%
1393
 
16.5%

fbs
Categorical

MISSING

Distinct2
Distinct (%)0.3%
Missing66
Missing (%)8.0%
Memory size6.6 KiB
0.0
631 
1.0
130 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2283
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0631
76.3%
1.0130
 
15.7%
(Missing)66
 
8.0%

Length

2022-10-17T22:35:58.986389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-17T22:35:59.044855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0631
82.9%
1.0130
 
17.1%

Most occurring characters

ValueCountFrequency (%)
01392
61.0%
.761
33.3%
1130
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1522
66.7%
Other Punctuation761
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01392
91.5%
1130
 
8.5%
Other Punctuation
ValueCountFrequency (%)
.761
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2283
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01392
61.0%
.761
33.3%
1130
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII2283
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01392
61.0%
.761
33.3%
1130
 
5.7%

restecg
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.4%
Missing4
Missing (%)0.5%
Memory size6.6 KiB
0.0
486 
2.0
178 
1.0
159 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2469
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0486
58.8%
2.0178
 
21.5%
1.0159
 
19.2%
(Missing)4
 
0.5%

Length

2022-10-17T22:35:59.096148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-17T22:35:59.159123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0486
59.1%
2.0178
 
21.6%
1.0159
 
19.3%

Most occurring characters

ValueCountFrequency (%)
01309
53.0%
.823
33.3%
2178
 
7.2%
1159
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1646
66.7%
Other Punctuation823
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01309
79.5%
2178
 
10.8%
1159
 
9.7%
Other Punctuation
ValueCountFrequency (%)
.823
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2469
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01309
53.0%
.823
33.3%
2178
 
7.2%
1159
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2469
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01309
53.0%
.823
33.3%
2178
 
7.2%
1159
 
6.4%

pro
Categorical

MISSING

Distinct2
Distinct (%)0.3%
Missing65
Missing (%)7.9%
Memory size6.6 KiB
0.0
647 
1.0
115 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2286
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0647
78.2%
1.0115
 
13.9%
(Missing)65
 
7.9%

Length

2022-10-17T22:35:59.215089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-17T22:35:59.275733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0647
84.9%
1.0115
 
15.1%

Most occurring characters

ValueCountFrequency (%)
01409
61.6%
.762
33.3%
1115
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1524
66.7%
Other Punctuation762
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01409
92.5%
1115
 
7.5%
Other Punctuation
ValueCountFrequency (%)
.762
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2286
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01409
61.6%
.762
33.3%
1115
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2286
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01409
61.6%
.762
33.3%
1115
 
5.0%

met
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct28
Distinct (%)3.9%
Missing107
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean7.3675
Minimum2
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-10-17T22:35:59.327413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q15
median7
Q39
95-th percentile13
Maximum18
Range16
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.097255167
Coefficient of variation (CV)0.4203943219
Kurtosis0.2787169346
Mean7.3675
Median Absolute Deviation (MAD)2
Skewness0.7197781468
Sum5304.6
Variance9.592989569
MonotonicityNot monotonic
2022-10-17T22:35:59.388994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
7107
12.9%
5106
12.8%
6102
12.3%
971
8.6%
1058
7.0%
454
6.5%
851
6.2%
331
 
3.7%
1327
 
3.3%
1223
 
2.8%
Other values (18)90
10.9%
(Missing)107
12.9%
ValueCountFrequency (%)
222
 
2.7%
2.51
 
0.1%
331
 
3.7%
3.51
 
0.1%
454
6.5%
4.51
 
0.1%
5106
12.8%
5.41
 
0.1%
5.81
 
0.1%
6102
12.3%
ValueCountFrequency (%)
182
 
0.2%
172
 
0.2%
167
 
0.8%
153
 
0.4%
1420
 
2.4%
1327
 
3.3%
1223
 
2.8%
1119
 
2.3%
1058
7.0%
971
8.6%

thalach
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct111
Distinct (%)14.4%
Missing57
Missing (%)6.9%
Infinite0
Infinite (%)0.0%
Mean139.5376623
Minimum69
Maximum202
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-10-17T22:35:59.463755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum69
5-th percentile98
Q1120
median140
Q3159.75
95-th percentile179
Maximum202
Range133
Interquartile range (IQR)39.75

Descriptive statistics

Standard deviation25.01259521
Coefficient of variation (CV)0.1792533628
Kurtosis-0.5570110255
Mean139.5376623
Median Absolute Deviation (MAD)20
Skewness-0.1806678604
Sum107444
Variance625.6299191
MonotonicityNot monotonic
2022-10-17T22:35:59.543759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15040
 
4.8%
14038
 
4.6%
12028
 
3.4%
13027
 
3.3%
16026
 
3.1%
17020
 
2.4%
11019
 
2.3%
12518
 
2.2%
14214
 
1.7%
16213
 
1.6%
Other values (101)527
63.7%
(Missing)57
 
6.9%
ValueCountFrequency (%)
691
 
0.1%
711
 
0.1%
731
 
0.1%
771
 
0.1%
802
0.2%
823
0.4%
843
0.4%
863
0.4%
871
 
0.1%
882
0.2%
ValueCountFrequency (%)
2021
 
0.1%
1951
 
0.1%
1941
 
0.1%
1921
 
0.1%
1902
0.2%
1882
0.2%
1871
 
0.1%
1862
0.2%
1854
0.5%
1844
0.5%

thalrest
Real number (ℝ≥0)

MISSING

Distinct73
Distinct (%)9.5%
Missing58
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean76.18335501
Minimum37
Maximum139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-10-17T22:35:59.626040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile56
Q166
median74
Q385
95-th percentile100
Maximum139
Range102
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.62398265
Coefficient of variation (CV)0.1919577137
Kurtosis0.7982786077
Mean76.18335501
Median Absolute Deviation (MAD)10
Skewness0.6582926207
Sum58585
Variance213.8608684
MonotonicityNot monotonic
2022-10-17T22:35:59.936855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7036
 
4.4%
8030
 
3.6%
7430
 
3.6%
6827
 
3.3%
7526
 
3.1%
8425
 
3.0%
7324
 
2.9%
6423
 
2.8%
7823
 
2.8%
7222
 
2.7%
Other values (63)503
60.8%
(Missing)58
 
7.0%
ValueCountFrequency (%)
371
 
0.1%
401
 
0.1%
431
 
0.1%
461
 
0.1%
471
 
0.1%
494
0.5%
502
 
0.2%
511
 
0.1%
528
1.0%
533
 
0.4%
ValueCountFrequency (%)
1391
 
0.1%
1341
 
0.1%
1253
0.4%
1241
 
0.1%
1203
0.4%
1191
 
0.1%
1161
 
0.1%
1152
 
0.2%
1122
 
0.2%
1105
0.6%

tpeakbps
Real number (ℝ≥0)

MISSING

Distinct73
Distinct (%)9.6%
Missing64
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean172.2961992
Minimum84
Maximum240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-10-17T22:36:00.015660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum84
5-th percentile130
Q1156
median170
Q3190
95-th percentile220
Maximum240
Range156
Interquartile range (IQR)34

Descriptive statistics

Standard deviation25.76185758
Coefficient of variation (CV)0.1495207538
Kurtosis0.1998375257
Mean172.2961992
Median Absolute Deviation (MAD)18
Skewness0.0428102665
Sum131462
Variance663.6733057
MonotonicityNot monotonic
2022-10-17T22:36:00.092402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18087
 
10.5%
16082
 
9.9%
17074
 
8.9%
19060
 
7.3%
20054
 
6.5%
15043
 
5.2%
14034
 
4.1%
22023
 
2.8%
21021
 
2.5%
16517
 
2.1%
Other values (63)268
32.4%
(Missing)64
 
7.7%
ValueCountFrequency (%)
841
 
0.1%
901
 
0.1%
921
 
0.1%
982
 
0.2%
1001
 
0.1%
1103
0.4%
1121
 
0.1%
1161
 
0.1%
1206
0.7%
1243
0.4%
ValueCountFrequency (%)
2405
 
0.6%
2351
 
0.1%
2321
 
0.1%
23014
1.7%
2281
 
0.1%
2241
 
0.1%
22023
2.8%
2161
 
0.1%
2153
 
0.4%
21021
2.5%

tpeakbpd
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct51
Distinct (%)6.7%
Missing64
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean87.42070773
Minimum11
Maximum134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-10-17T22:36:00.179259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile64
Q180
median90
Q3100
95-th percentile110
Maximum134
Range123
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.05719423
Coefficient of variation (CV)0.1722383017
Kurtosis0.8726135898
Mean87.42070773
Median Absolute Deviation (MAD)10
Skewness-0.1655087739
Sum66702
Variance226.7190982
MonotonicityNot monotonic
2022-10-17T22:36:00.264127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80136
16.4%
90120
14.5%
100108
13.1%
7052
 
6.3%
11040
 
4.8%
9524
 
2.9%
7523
 
2.8%
6022
 
2.7%
7822
 
2.7%
8416
 
1.9%
Other values (41)200
24.2%
(Missing)64
 
7.7%
ValueCountFrequency (%)
111
 
0.1%
261
 
0.1%
402
 
0.2%
451
 
0.1%
502
 
0.2%
551
 
0.1%
562
 
0.2%
583
 
0.4%
6022
2.7%
623
 
0.4%
ValueCountFrequency (%)
1341
 
0.1%
1302
 
0.2%
12015
 
1.8%
1184
 
0.5%
1162
 
0.2%
1157
 
0.8%
1142
 
0.2%
1121
 
0.1%
11040
4.8%
1082
 
0.2%

trestbpd
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct32
Distinct (%)4.2%
Missing60
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean83.54628422
Minimum50
Maximum110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-10-17T22:36:00.344108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile70
Q180
median80
Q390
95-th percentile100
Maximum110
Range60
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.639460546
Coefficient of variation (CV)0.1153786866
Kurtosis-0.001920627627
Mean83.54628422
Median Absolute Deviation (MAD)10
Skewness-0.004938102035
Sum64080
Variance92.91919962
MonotonicityNot monotonic
2022-10-17T22:36:00.415991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
80241
29.1%
90148
17.9%
7082
 
9.9%
10059
 
7.1%
8527
 
3.3%
7824
 
2.9%
9521
 
2.5%
8215
 
1.8%
8414
 
1.7%
7514
 
1.7%
Other values (22)122
14.8%
(Missing)60
 
7.3%
ValueCountFrequency (%)
502
 
0.2%
581
 
0.1%
6010
 
1.2%
644
 
0.5%
654
 
0.5%
661
 
0.1%
684
 
0.5%
7082
9.9%
728
 
1.0%
749
 
1.1%
ValueCountFrequency (%)
1104
 
0.5%
1062
 
0.2%
1053
 
0.4%
1041
 
0.1%
1021
 
0.1%
10059
7.1%
9812
 
1.5%
967
 
0.8%
9521
 
2.5%
9412
 
1.5%

exang
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.3%
Missing57
Missing (%)6.9%
Memory size6.6 KiB
0.0
481 
1.0
289 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2310
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0481
58.2%
1.0289
34.9%
(Missing)57
 
6.9%

Length

2022-10-17T22:36:00.489457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-17T22:36:00.552941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0481
62.5%
1.0289
37.5%

Most occurring characters

ValueCountFrequency (%)
01251
54.2%
.770
33.3%
1289
 
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1540
66.7%
Other Punctuation770
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01251
81.2%
1289
 
18.8%
Other Punctuation
ValueCountFrequency (%)
.770
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01251
54.2%
.770
33.3%
1289
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01251
54.2%
.770
33.3%
1289
 
12.5%

oldpeak
Real number (ℝ)

HIGH CORRELATION
MISSING
ZEROS

Distinct51
Distinct (%)6.6%
Missing60
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean0.9065189048
Minimum-2.6
Maximum6.2
Zeros322
Zeros (%)38.9%
Negative9
Negative (%)1.1%
Memory size6.6 KiB
2022-10-17T22:36:00.631087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-2.6
5-th percentile0
Q10
median0.6
Q31.6
95-th percentile3
Maximum6.2
Range8.8
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.091713032
Coefficient of variation (CV)1.204291522
Kurtosis1.072110363
Mean0.9065189048
Median Absolute Deviation (MAD)0.6
Skewness1.024891266
Sum695.3
Variance1.191837344
MonotonicityNot monotonic
2022-10-17T22:36:00.713751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0322
38.9%
173
 
8.8%
266
 
8.0%
1.545
 
5.4%
327
 
3.3%
2.516
 
1.9%
1.415
 
1.8%
0.515
 
1.8%
1.614
 
1.7%
0.614
 
1.7%
Other values (41)160
19.3%
(Missing)60
 
7.3%
ValueCountFrequency (%)
-2.61
 
0.1%
-1.51
 
0.1%
-1.11
 
0.1%
-11
 
0.1%
-0.91
 
0.1%
-0.81
 
0.1%
-0.71
 
0.1%
-0.51
 
0.1%
-0.11
 
0.1%
0322
38.9%
ValueCountFrequency (%)
6.21
 
0.1%
5.61
 
0.1%
51
 
0.1%
4.22
 
0.2%
47
0.8%
3.81
 
0.1%
3.71
 
0.1%
3.64
0.5%
3.52
 
0.2%
3.42
 
0.2%

rldv5e
Real number (ℝ≥0)

MISSING

Distinct121
Distinct (%)16.0%
Missing71
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean13.475
Minimum2
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-10-17T22:36:00.806393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5.875
Q19.775
median13
Q317
95-th percentile23
Maximum36
Range34
Interquartile range (IQR)7.225

Descriptive statistics

Standard deviation5.408822508
Coefficient of variation (CV)0.4013968466
Kurtosis0.1574806757
Mean13.475
Median Absolute Deviation (MAD)4
Skewness0.5151015957
Sum10187.1
Variance29.25536093
MonotonicityNot monotonic
2022-10-17T22:36:00.890971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1149
 
5.9%
2046
 
5.6%
1041
 
5.0%
1540
 
4.8%
1337
 
4.5%
1235
 
4.2%
935
 
4.2%
1734
 
4.1%
1429
 
3.5%
1829
 
3.5%
Other values (111)381
46.1%
(Missing)71
 
8.6%
ValueCountFrequency (%)
21
 
0.1%
2.43
 
0.4%
2.81
 
0.1%
34
0.5%
3.51
 
0.1%
49
1.1%
4.11
 
0.1%
4.51
 
0.1%
4.71
 
0.1%
4.81
 
0.1%
ValueCountFrequency (%)
361
 
0.1%
303
 
0.4%
292
 
0.2%
282
 
0.2%
275
0.6%
264
0.5%
25.31
 
0.1%
25.21
 
0.1%
258
1.0%
247
0.8%

num
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.6%
Missing2
Missing (%)0.2%
Memory size6.6 KiB
0.0
403 
1.0
168 
2.0
108 
3.0
107 
4.0
 
39

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2475
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row3.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0403
48.7%
1.0168
20.3%
2.0108
 
13.1%
3.0107
 
12.9%
4.039
 
4.7%
(Missing)2
 
0.2%

Length

2022-10-17T22:36:00.966635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-17T22:36:01.036685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0403
48.8%
1.0168
20.4%
2.0108
 
13.1%
3.0107
 
13.0%
4.039
 
4.7%

Most occurring characters

ValueCountFrequency (%)
01228
49.6%
.825
33.3%
1168
 
6.8%
2108
 
4.4%
3107
 
4.3%
439
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1650
66.7%
Other Punctuation825
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01228
74.4%
1168
 
10.2%
2108
 
6.5%
3107
 
6.5%
439
 
2.4%
Other Punctuation
ValueCountFrequency (%)
.825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2475
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01228
49.6%
.825
33.3%
1168
 
6.8%
2108
 
4.4%
3107
 
4.3%
439
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII2475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01228
49.6%
.825
33.3%
1168
 
6.8%
2108
 
4.4%
3107
 
4.3%
439
 
1.6%

dataset
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.6 KiB
hungarian
295 
cleveland
282 
long-beach-va
200 
switzerland
50 

Length

Max length13
Median length9
Mean length10.08827086
Min length9

Characters and Unicode

Total characters8343
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhungarian
2nd rowhungarian
3rd rowhungarian
4th rowhungarian
5th rowhungarian

Common Values

ValueCountFrequency (%)
hungarian295
35.7%
cleveland282
34.1%
long-beach-va200
24.2%
switzerland50
 
6.0%

Length

2022-10-17T22:36:01.103611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-17T22:36:01.176900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
hungarian295
35.7%
cleveland282
34.1%
long-beach-va200
24.2%
switzerland50
 
6.0%

Most occurring characters

ValueCountFrequency (%)
a1322
15.8%
n1122
13.4%
e814
9.8%
l814
9.8%
h495
 
5.9%
g495
 
5.9%
c482
 
5.8%
v482
 
5.8%
-400
 
4.8%
r345
 
4.1%
Other values (9)1572
18.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7943
95.2%
Dash Punctuation400
 
4.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1322
16.6%
n1122
14.1%
e814
10.2%
l814
10.2%
h495
 
6.2%
g495
 
6.2%
c482
 
6.1%
v482
 
6.1%
r345
 
4.3%
i345
 
4.3%
Other values (8)1227
15.4%
Dash Punctuation
ValueCountFrequency (%)
-400
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7943
95.2%
Common400
 
4.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1322
16.6%
n1122
14.1%
e814
10.2%
l814
10.2%
h495
 
6.2%
g495
 
6.2%
c482
 
6.1%
v482
 
6.1%
r345
 
4.3%
i345
 
4.3%
Other values (8)1227
15.4%
Common
ValueCountFrequency (%)
-400
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII8343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1322
15.8%
n1122
13.4%
e814
9.8%
l814
9.8%
h495
 
5.9%
g495
 
5.9%
c482
 
5.8%
v482
 
5.8%
-400
 
4.8%
r345
 
4.1%
Other values (9)1572
18.8%

Interactions

2022-10-17T22:35:56.757080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:49.023572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:49.759223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:50.488276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:51.241530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:52.000182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:52.944014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:53.650167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:54.401662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:55.135481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:55.852295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:56.817388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:49.085600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:49.825456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:50.561291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:51.303558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:52.063179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:53.003827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:53.713738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:54.464948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:55.196912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:55.910445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:56.877749image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:49.146677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:49.887705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:50.632679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:51.367825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:52.126181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:53.065010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:53.778350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:54.527911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:55.259727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:55.968673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:56.943183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:49.212273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:49.953871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:50.706619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:51.432305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:52.192557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:53.131263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:53.847092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:54.594488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:55.324464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:56.030339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:57.015365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:49.279851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:50.021625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:50.779154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:51.501945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:52.262148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:53.200817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:53.917954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:54.664758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:55.392843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:56.096915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:57.080772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:49.345312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:50.087690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:50.845762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:51.567984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:52.548924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:53.267704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:53.988175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:54.733114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:55.458843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:56.159679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:57.143285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:49.409330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:50.151272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:50.911826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:51.633758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:52.612016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:53.329631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:54.059603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:54.806484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:55.521672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:56.444032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:57.212515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:49.491261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:50.219789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:50.981433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:51.710826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:52.682341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:53.397824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:54.131716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:54.878526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:55.591990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:56.510409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:57.277896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:49.564378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:50.285124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:51.047345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:51.781060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:52.747622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:53.462287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:54.200448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:54.944439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:55.658330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:56.574482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:57.343380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:49.629531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:50.350564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:51.114611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:51.855592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:52.814327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:53.526841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:54.268945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:55.009727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:55.724799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:56.637816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:57.404186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:49.693666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:50.416383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:51.176488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:51.929627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:52.876526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:53.586217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:54.333691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:55.070305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:55.786713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T22:35:56.695626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-10-17T22:36:01.245553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-17T22:36:01.369067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-17T22:36:01.500376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-17T22:36:01.626481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-17T22:36:01.729350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-17T22:35:57.521636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-17T22:35:57.714965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-10-17T22:35:57.863980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-10-17T22:35:58.053585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexagesexcptrestbpshtnfbsrestecgprometthalachthalresttpeakbpstpeakbpdtrestbpdexangoldpeakrldv5enumdataset
0040.01.02.0140.00.00.00.00.07.0172.086.0200.0110.086.00.00.020.00.0hungarian
1149.00.03.0160.01.00.00.00.07.0156.0100.0220.0106.090.00.01.013.01.0hungarian
2237.01.02.0130.00.00.01.00.05.098.058.0180.0100.080.00.00.014.00.0hungarian
3348.00.04.0138.00.00.00.00.04.0108.054.0210.0106.086.01.01.522.03.0hungarian
4454.01.03.0150.00.00.00.01.03.0122.074.0130.0100.090.00.00.09.00.0hungarian
5539.01.03.0120.00.00.00.00.08.0170.086.0198.0100.080.00.00.021.00.0hungarian
6645.00.02.0130.00.00.00.00.010.0170.090.0200.0106.084.00.00.011.00.0hungarian
7754.01.02.0110.00.00.00.00.07.0142.056.0220.070.070.00.00.011.00.0hungarian
8837.01.04.0140.01.00.00.00.07.0130.063.0190.0100.080.01.01.519.01.0hungarian
9948.00.02.0120.00.00.00.00.04.0120.072.0140.080.080.00.00.06.00.0hungarian

Last rows

df_indexagesexcptrestbpshtnfbsrestecgprometthalachthalresttpeakbpstpeakbpdtrestbpdexangoldpeakrldv5enumdataset
81789162.01.04.0160.01.01.01.01.02.5108.069.0160.090.080.01.03.019.04.0long-beach-va
81889253.01.04.0144.01.01.01.00.05.0128.076.0150.0102.094.01.01.513.03.0long-beach-va
81989362.01.04.0158.01.00.01.00.08.0138.086.0202.098.090.01.00.022.01.0long-beach-va
82089446.01.04.0134.01.00.00.00.07.0126.088.0174.0114.090.00.00.07.02.0long-beach-va
82189554.00.04.0127.00.01.01.00.08.0154.083.0158.084.078.00.00.020.01.0long-beach-va
82289662.01.01.0NaN0.00.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0long-beach-va
82389755.01.04.0122.01.01.01.00.05.0100.074.0210.0100.070.00.00.04.02.0long-beach-va
82489858.01.04.0NaN0.01.02.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0long-beach-va
82589962.01.02.0120.01.00.02.00.07.093.067.0164.0110.080.01.00.017.01.0long-beach-va
826900NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNlong-beach-va